SpreadMRI: Ultra-Fast, Spread-Spectrum Magnetic Resonance Imaging
ERC Advanced Grant 834940
Imaging speed is a key factor to capture rapid changes at high spatial and temporal resolution. A major limitation of magnetic resonance (MR) imaging is its rather low speed compared to other modalities like ultra sound or computerized tomography. We aim to explore two novel concepts to boost MR imaging speed by another order of magnitude compared to existing techniques. SpreadMRI fundamentally steps beyond current concepts of image encoding by exploiting a spectral spin modulation that so far has not been utilized. SpreadMRI is based on the rapid and local modulation of magnetic fields produced by current loops and/or radiofrequency (RF) loops. Applied spectral modulations are in the MHz range bridging the low-frequency band of switched gradients (kHz) and the 100 MHz range of the Larmor frequency. SpreadMRI spreads the bandwidth of gradient-encoded spin frequencies using distinct carrier frequencies originating from a certain region of the object. This spatially unique information will then be used to disentangle parts of the object, and thus to drastically boost imaging speed.
Approaching this intermediate frequency band requires to address several basic research questions related to image reconstruction, electromagnetic coupling, spin Physics and possible biological effects. Based on theoretical analysis and exhaustive electromagnetic simulations of dedicated current loop and RF coil arrangements, including variants of different modulation patterns, several types of SpreadMRI coils for human head imaging at 9.4T will be developed and applied for high temporal and spatial functional brain imaging. The specific approach of SpreadMRI will lead to major changes in the hard- and software environment of current MR-scanners. It will not only provide new insight within the areas covered by the proposal, but will definitely benefit conventional MR diagnostic by enabling new applications with a simultaneous reduction of motion artifacts and increased patient throughput.
Acknowledgement: This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 834940)